Digital or conventional film-based photographic images of medical samples are often analyzed for the purposes of diagnosis. The images are examined for abnormal appearances and structures, lesions and other signs of pathological changes. The pathological characterization may be based on intensity, color, size, shape, or any combination of these characteristics. It is essential that characterization of pathological characteristics is precise in order to diagnose the underlying disease correctly. Furthermore, it is often desirable to monitor the changes in the appearance of lesions to determine the progression of diseases or the results of treatments. However, interpretation of images of medical samples is difficult when lesions or anatomical structures are superimposed on a background that is not even in intensity or one that is partially obscured.
Of all the organs of the body, the eye presents a unique challenge as far as diagnosis of disease is concerned. It is usually not possible to take samples from the delicate tissues inside the eye because this could be detrimental to vision. Fortuitously, many structures inside the eye can be visually observed through the transparent cornea at the front of the eye using a variety of diagnostic instruments. This is especially true for determining the condition of the retina that is located in the back of the eye, directly opposite the transparent cornea, and which is the main structure where images essential to vision are formed.
Present characterization of retinal lesions is manual and in many cases subjective, and attempts at automated and computerized analysis have provided very limited results so far. Manual analysis relies on trained observers who must master the subtleties of the intensity, geometry, and color characteristics of retinal lesions. A significant complication in evaluating these lesions is the fact that they are present on a background that is not uniform, but varies in color or intensity. This non-uniform background makes the same lesion appear lighter or darker, or changes its apparent size or geometry, depending where on the retina it is located. This unevenness is caused by variability of the optical characteristics of the adjoining retinal anatomic structures. Misinterpretation of these lesions may lead to incorrect diagnosis.
Tracking small changes is important in cases where the effects of drug therapy or laser surgery have to be evaluated to provide guidance as to their suitability. Manual analysis only provides quantification of pathology on a rough scale. Furthermore, determining small changes is often difficult if not impossible.
Progressive loss of vision has many causes. In the case of age-related macular degeneration (AMD), lesions such as drusen and Geographic Atrophy (GA) are mostly present on the macula, but may be present in other parts of the retina as well. Drusen are yellowish-white subretinal deposits that often have poorly defined borders. GA lesions are hypopigmented areas that are darker than the background and often present themselves as complex shapes on photographs and autofluorescent (AF) images. They appear as regions of varying size and color when examined using any one of a variety of ophthalmologic diagnostic instruments.
There are many other retinal diseases besides AMD, and all of these are characterized by unique lesions that may be present on the macula or on other parts of the retina. Analysis of these may also be facilitated by our methods.
A number of ophthalmologic diagnostic instruments may be used to determine the extent of retinal pathology. One such instrument is the scanning laser ophthalmoscope (SLO). It uses monochromatic laser light and confocal optics to image the retina in a number of modalities and provides an output in the form of monochromatic digital images. Evaluation of these images is complicated by the fact that in most modes of employing this instrument the retinal background is uneven. When this instrument is used in the AF mode, typically the normal macular background is darker (hypofluorescent) in the center and lighter (hyperfluorescent) away from the center. This causes identical areas of pathology to appear darker in the hypofluorescent center and brighter in the hyperfluorescent portions of the macula.
Another instrument is the fundus camera. It is used to photograph the retina using white light provided by a flash lamp. The resulting image is a color photograph, usually in the form of a 35 mm slide. On these images GA appears as reddish or hypopigmented patches of varying size superimposed on a pink background. These lesions are evaluated by trained technicians who determine the area and number of drusen or GA based on their subtle differences in size, number, or color and then assign a grade to these lesions. In spite of the extensive amount of training and experience, precise quantification is rarely possible and errors between trained graders are common.
There has been continued interest in the use of digital techniques for automated quantification of macular pathology, particularly drusen and GA. The system of manual classification of patients by stage of AMD based on painstaking analysis of drusen size, number, area, and morphology is labor intensive and costly, and there is increasing interest in developing automated methods to replace the manual ones.
Unfortunately, despite some progress, none of the automated methods developed so far have gained widespread use. A major obstacle is the non-uniform background that prior methods have so far been unable to deal with. For example, some automated methods sometimes incorrectly interpreted perivascular structures exhibiting bimodal distributions as coming from perivascular drusen. In other cases, large areas of background were included due to incorrect choices of threshold. These sources of error required many operator interventions to correct. The resulting methods were capable of good reproducibility, but were too tedious for general use. Hence, for almost two decades the limiting factor was not the complexity of computer algorithms, but the fact that the methods themselves were tied to local reflectance calculations. For example, for drusen segmentation, the determination of the area of this pathological feature is based on specific characteristics or patterns that distinguish it from the background. Generally, a post-processing step is necessary to correct errors or enlarge incompletely segmented drusen to achieve acceptable accuracy. Additionally, these methods are based on segmentation by local histograms and threshold techniques that have serious deficiencies.
Still other methods use other morphological operators or vary local histogram criteria for threshold choice to try to correct for deficiencies related to segmentation. These criteria may involve kurtosis or skewness. However, no matter how many histogram-based criteria are employed for local segmentation, widely different combinations of image features (drusen and background) can yield the same histogram. An extreme example of this is where regions that are either all background or all drusen yield the same mesokurtic nonskewed distribution. The solution to this has been a morphological dilation operator, which is intended in all drusen cases to distort the local histogram by reintroducing background and thereby improve threshold recognition. Unfortunately, this artificial operator does not always perform as intended. What is needed are methods to efficiently generate uniform backgrounds so that precise measurement of images can be attained.